SoroushVahidi/scidocs huggingface dataset:
Comparing a Clipmap to a Sparse Voxel Octree for Global Illumination
(2017)

Voxel cone tracing is a real-time method that approximates global illumination using a voxel approximation of the original scene. However, a high-resolution voxel approximation, which is necessary for good quality, consumes much memory, and a compact data structure for storing the voxels is necessary. In this thesis, as a primary contribution, we provide a comparison of two such data structures: a Sparse Voxel Octree, and a Clipmap. We implement these two data structures, and provide detailed descriptions of both with many important implementation details. These descriptions are much more complete than what exists in the current literature, and it is the secondary contribution of this thesis. In the comparison, we find that the octree performs worse than the clipmap with respect to memory consumption and performance, due to the overhead introduced by the complex octree data structure. However, with respect to visual quality, the octree is the superior choice, since the clipmap does not provide the same voxel resolution everywhere.
SuBiC: A Supervised, Structured Binary Code for Image Search
(2017)

For large-scale visual search, highly compressed yet meaningful representations of images are essential. Structured vector quantizers based on product quantization and its variants are usually employed to achieve such compression while minimizing the loss of accuracy. Yet, unlike binary hashing schemes, these unsupervised methods have not yet benefited from the supervision, end-to-end learning and novel architectures ushered in by the deep learning revolution. We hence propose herein a novel method to make deep convolutional neural networks produce supervised, compact, structured binary codes for visual search. Our method makes use of a novel block-softmax nonlinearity and of batch-based entropy losses that together induce structure in the learned encodings. We show that our method outperforms state-of-the-art compact representations based on deep hashing or structured quantization in single and cross-domain category retrieval, instance retrieval and classification. We make our code and models publicly available online.
A methodology for power characterization of associative memories
(2015)

Content Addressable Memories (CAM) have become increasingly more important in applications requiring high speed memory search due to their inherent massively parallel processing architecture. We present a complete power analysis methodology for CAM systems to aid the exploration of their power-performance trade-offs in future systems. Our proposed methodology uses detailed transistor level circuit simulation of power behavior and a handful of input data types to simulate full chip power consumption. Furthermore, we applied our power analysis methodology on a custom designed associative memory test chip. This chip was developed by Fermilab for the purpose of developing high performance real-time pattern recognition on high volume data produced by a future large-scale scientific experiment. We applied our methodology to configure a power model for this test chip. Our model is capable of predicting the total average power within 4% of actual power measurements. Our power analysis methodology can be generalized and applied to other CAM-like memory systems and accurately characterize their power behavior.
A data-driven approach to exploring similarities of tourist attractions through online reviews
(2018)

The motivation for tourists to visit a city is often driven by the uniqueness of the attractions accessible within the region. The draw to these locations varies by visitor as some travelers are interested in a single specific attraction while others prefer thematic travel. Tourists today have access to detailed experiences of other visitors to these locations in the form of user-contributed text reviews, opinions, photographs, and videos, all contributed through online tourism platforms. The data available through these platforms offer a unique opportunity to examine the similarities and difference between these attractions, their cities, and the visitors that contribute the reviews. In this work we take a data-driven approach to assessing similarity through textual analysis of user-contributed reviews, uncovering nuanced differences and similarities in the ways that reviewers write about attractions and cities.
An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective
(2017)

Given a question and a set of answer candidates, answer triggering determines whether the candidate set contains any correct answers. If yes, it then outputs a correct one. In contrast to existing pipeline methods which first consider individual candidate answers separately and then make a prediction based on a threshold, we propose an end-to-end deep neural network framework, which is trained by a novel group-level objective function that directly optimizes the answer triggering performance. Our objective function penalizes three potential types of error and allows training the framework in an end-to-end manner. Experimental results on the WIKIQA benchmark show that our framework outperforms the state of the arts by a 6.6% absolute gain under F1 measure1.
Truncating Temporal Differences: On the Efficient Implementation of TD(lambda) for Reinforcement Learning
(1995)

Temporal diierence (TD) methods constitute a class of methods for learning predictions in multi-step prediction problems, parameterized by a recency factor. Currently the most important application of these methods is to temporal credit assignment in reinforcement learning. Well known reinforcement learning algorithms, such as AHC or Q-learning, may be viewed as instances of TD learning. This paper examines the issues of the eecient and general implementation of TD() for arbitrary , for use with reinforcement learning algorithms optimizing the discounted sum of rewards. The traditional approach, based on eligibility traces, is argued to suuer from both ineeciency and lack of generality. The TTD (Truncated Temporal Diierences) procedure is proposed as an alternative, that indeed only approximates TD(), but requires very little computation per action and can be used with arbitrary function representation methods. The idea from which it is derived is fairly simple and not new, but probably unexplored so far. Encouraging experimental results are presented, suggesting that using > 0 with the TTD procedure allows one to obtain a signiicant learning speedup at essentially the same cost as usual TD(0) learning.
Ultrasound-Guided Fenestration of the Carpal Ligament Using a Double-Needle Approach.
(2017)

Injection techniques for carpal tunnel syndrome have evolved from landmark palpation injection techniques to more accurate ultrasound-guided approaches. Presented is a case report describing a technique serendipitously discovered during a carpal ligament fenestration. The case involved a 66-year-old man with a diagnosis of carpal tunnel syndrome. After a failed attempt at treatment using a wrist splint and activity modification, he was treated by median nerve hydrodissection with 100% temporary pain relief. When his symptoms recurred, a carpal tunnel combined hydrodissection/fenestration technique was performed. Because of difficulty extricating the carpal ligament from the median nerve with the first needle, which was placed longitudinal to the median nerve, a second needle was placed transverse to the median nerve to aid in hydrodissection. The second needle was left in because it was found to be helpful in maintaining a safe distance between the median nerve and the carpal ligament by intermittent injection through the second needle. The patient reported 70% relief of his symptoms at 2-week follow-up and 50% sustained relief at 3 months. A 2-needle technique is feasible and can be helpful during median nerve hydrodissection/carpal ligament fenestration when technical or anatomical issues arise preventing treatment using the traditional single needle approach. This double needle approach allows for use of injectate to maintain separation between the median nerve and the ligament during the fenestration.
When does Consumer Empowerment Lead to Satisfied Customers? Some Mediating and Moderating Effects of the Empowerment-Satisfaction Link
(2009)

Technological advances increasingly provide marketers with the opportunity to empower consumers. Consumer empowerment is a positive subjective state evoked by consumer perceptions of increasing control. As a positive state, increasing consumer empowerment should be associated with increasing consumer satisfaction. If such a relationship exists, it may be influenced by a number of contextual variables. Knowing in what contexts empowerment has a greater impact on satisfaction would help marketers decide when they could more effectively use such a strategy. This study has two purposes: 1) to investigate the relationship between consumer empowerment and satisfaction and 2) to investigate a set of potential influences on that relationship. Marketers will be in a better position to decide when to empower consumers if they have guidance on the relationships between these variables.
Linear codes over F2+uF2+vF2+uvF2
(2010)

In this work, we investigate linear codes over the ring F2 + uF2 + vF2 + uvF2. We first analyze the structure of the ring and then define linear codes over this ring which turns out to be a ring that is not finite chain or principal ideal contrary to the rings that have hitherto been studied in coding theory. Lee weights and Gray maps for these codes are defined by extending on those introduced in works such as Betsumiya et al. (Discret Math 275:43–65, 2004) and Dougherty et al. (IEEE Trans Inf 45:32–45, 1999). We then characterize the F2 +uF2 +vF2 +uvF2-linearity of binary codes under the Gray map and give a main class of binary codes as an example of F2 + uF2 + vF2 + uvF2-linear codes. The duals and the complete weight enumerators for F2 + uF2 + vF2 + uvF2-linear codes are also defined after which MacWilliams-like identities for complete and Lee weight enumerators as well as for the ideal decompositions of linear codes over F2 + uF2 + vF2 + uvF2 are obtained.
AIMED- A Personalized TV Recommendation System
(2007)

Previous personalized DTV recommendation systems focus only on viewers’ historical viewing records or demographic data. This study proposes a new recommending mechanism from a user oriented perspective. The recommending mechanism is based on user properties such as Activities, Interests, Moods, Experiences, and Demographic information—AIMED. The AIMED data is fed into a neural network model to predict TV viewers’ program preferences. Evaluation results indicate that the AIMED model significantly increases recommendation accuracy and decreases prediction errors compared to the conventional model.
Proportional smile design using the recurring esthetic dental (red) proportion.
(2001)

Dentists have needed an objective way in which to evaluate a smile. A method for determining the ideal size and position of the anterior teeth has been presented here. Use of the FIVE to evaluate the RED proportion and the width-to-height ratio, tempered with sound clinical judgment, gives pleasing and consistent results. With the diversity that exists in nature, rarely does the final result follow all the mathematical rules of proportional smile design. This approach may serve as a foundation on which to base initial smile design, however. When one begins to understand the relationship between beauty, mathematics, and the surrounding world, one begins to appreciate their interdependence.
Scientific workflow management and the Kepler system
(2005)

Many scientific disciplines are now data and information driven, and new scientific knowledge is often gained by scientists putting together data analysis and knowledge discovery “pipelines”. A related trend is that more and more scientific communities realize the benefits of sharing their data and computational services, and are thus contributing to a distributed data and computational community infrastructure (a.k.a. “the Grid”). However, this infrastructure is only a means to an end and scientists ideally should be bothered little with its existence. The goal is for scientists to focus on development and use of what we call scientific workflows. These are networks of analytical steps that may involve, e.g., database access and querying steps, data analysis and mining steps, and many other steps including computationally intensive jobs on high performance cluster computers. In this paper we describe characteristics of and requirements for scientific workflows as identified in a number of our application projects. We then elaborate on Kepler, a particular scientific workflow system, currently under development across a number of scientific data management projects. We describe some key features of Kepler and its underlying Ptolemy ii system, planned extensions, and areas of future research. Kepler is a communitydriven, open source project, and we always welcome related projects and new contributors to join. ∗Work supported by NSF/ITR 0225676 (SEEK), DOE SciDAC DE-FC02-01ER25486 (SDM), NSF/ITR CCR-00225610 (Chess), NSF/ITR 0225673 (GEON), NIH/NCRR 1R24 RR019701-01 Biomedical Informatics Research Network Coordinating Center (BIRN-CC), NSF/ITR 0325963 (ROADNet), NSF/DBI-0078296 (Resurgence) †San Diego Supercomputer Center, UC San Diego; ?Dept. of Computer Science & Genome Center, UC Davis; ‡National Center for Ecological Analysis and Synthesis, UC Santa Barbara; and §Department of Electrical Engineering and Computer Sciences, UC Berkeley
A Taxonomy of Workflow Management Systems for Grid Computing
(2005)

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.
Computation for ChIP-seq and RNA-seq studies
(2009)

Genome-wide measurements of protein-DNA interactions and transcriptomes are increasingly done by deep DNA sequencing methods (ChIP-seq and RNA-seq). The power and richness of these counting-based measurements comes at the cost of routinely handling tens to hundreds of millions of reads. Whereas early adopters necessarily developed their own custom computer code to analyze the first ChIP-seq and RNA-seq datasets, a new generation of more sophisticated algorithms and software tools are emerging to assist in the analysis phase of these projects. Here we describe the multilayered analyses of ChIP-seq and RNA-seq datasets, discuss the software packages currently available to perform tasks at each layer and describe some upcoming challenges and features for future analysis tools. We also discuss how software choices and uses are affected by specific aspects of the underlying biology and data structure, including genome size, positional clustering of transcription factor binding sites, transcript discovery and expression quantification.
Distributed computing in practice: the Condor experience
(2005)

Since 1984, the Condor project has enabled ordinary users to do extraordinary computing. Today, the project continues to explore the social and technical problems of cooperative computing on scales ranging from the desktop to the world-wide computational Grid. In this paper, we provide the history and philosophy of the Condor project and describe how it has interacted with other projects and evolved along with the field of distributed computing. We outline the core components of the Condor system and describe how the technology of computing must correspond to social structures. Throughout, we reflect on the lessons of experience and chart the course travelled by research ideas as they grow into production systems. Copyright c © 2005 John Wiley & Sons, Ltd.
Effects of playing a violent video game as male versus female avatar on subsequent aggression in male and female players.
(2014)

Previous research has shown that violent video games can increase aggression in players immediately after they play. The present research examines the effects of one subtle cue within violent video games that might moderate these effects-whether the avatar is male or female. One common stereotype is that males are more aggressive than females. Thus, playing a violent video game as a male avatar, compared to a female avatar, should be more likely to prime aggressive thoughts and inclinations in players and lead to more aggressive behavior afterwards. Male and female university students (N = 242) were randomly assigned to play a violent video game as a male or female avatar. After gameplay, participants gave an ostensible partner who hated spicy food hot sauce to eat. The amount of hot sauce given was used to measure aggression. Consistent with priming theory, results showed that both male and female participants who played a violent game as a male avatar behaved more aggressively afterwards than those who played as female avatar. The priming effects of the male avatar were somewhat stronger for male participants than for female participants, suggesting that male participants identified more with the male avatar than did the female participants. These results are particularly noteworthy because they are consistent with another recent experiment showing that playing a violent game as an avatar with a different stereotypically aggressive attribute (black skin color) stimulates more aggression than playing as an avatar without the stereotypically aggressive attribute (Yang et al., 2014, Social Psychological and Personality Science).
Meta BCI : Hippocampus-striatum network inspired architecture towards flexible BCI
(2018)

Classifying neural signals is a crucial step in the brain-computer interface (BCI). Although Deep Neural Network (DNN) has been shown to be surprisingly good at classification, DNN suffers from long training time and catastrophic forgetting. Catastrophic forgetting refers to a phenomenon in which a DNN tends to forget previously learned task when it learns a new task. Here we argue that the solution to this problem may be found in the human brain, specifically, by combining functions of the two regions: the striatum and the hippocampus, which is pivotal for reinforcement learning and memory recall relevant to the current context, respectively. The mechanism of these brain regions provides insights into resolving catastrophic forgetting and long training time of DNNs. Referring to the hippocampus-striatum network we discuss design principles of combining different types of DNNs for building a new BCI architecture, called “Meta BCI”.
A smart parking system to minimize searching time, fuel consumption and CO2 emission
(2017)

This paper proposes a Smart Parking System (SPS) based on Internet of Thing (IoT) and Wireless Sensors Network (WSN). It is assumed that Global Positioning System (GPS) and wireless communication system is placed in the vehicle. GPS gets the position of vehicle and wireless communication send it to server. A smartphone uses for user to remotely view the parking, status of spots and book a slot manually. The proposed system decreases the searching time of free slot by giving route to free slot. As the fuel consumption and carbon dioxide (CO2) emission decreases by decreasing the distance and wise versa because there is direct relationship between them. It is shown in simulations result at the end of the paper that the objectives of the paper to minimize searching time, fuel consumption and CO2 emission achieved.
MapReduce as a programming model for association rules algorithm on Hadoop
(2010)

As association rules widely used, it needs to study many problems, one of which is the generally larger and multi-dimensional datasets, and the rapid growth of the mount of data. Single-processor's memory and CPU resources are very limited, which makes the algorithm performance inefficient. Recently the development of network and distributed technology makes cloud computing a reality in the implementation of association rules algorithm. In this paper we describe the improved Apriori algorithm based on MapReduce mode, which can handle massive datasets with a large number of nodes on Hadoop platform.
Design of a linear variable-stiffness mechanism using preloaded bistable beams
(2014)

A machine with variable internal stiffness can meet the performance requirement in different working environments. Linear variable-stiffness mechanisms (LVSM) are rarely found compared with those producing rotary motion. This paper presents an approach to design a linear variable-stiffness mechanism. The idea is to parallel connect two lateral bistable beams with a vertical spring. Through the preload adjustment of the bistable beams, the output force to displacement curve can exhibit different stiffnesses. The merit of the proposed LVSM is that very large stiffness variation can be achieved in a compact space. The stiffness may even be tuned to zero by assigning a proper stiffness to the vertical spring. An optimization formulation is presented to design a LVSM with the largest stiffness variation. The effects of various parameters on the stiffness variation and linearity are discussed. The results are numerically verified with a prototype illustrated.
Performance Sensitivity of Space Sharing Processor Scheduling in Distributed-Memory Multicomputers
(1998)

Processor scheduling in distributed-memory systems has received considerable attention in recent years. Several commercial distributed-memory systems use spacesharing processor scheduling. In space-sharing, the set of processors in a system is partitioned and each partition is assigned for the exclusive use of a job. Space-sharing policies can be divided into fixed, static, or dynamic categories. For distributed-memory systems, dynamic policies incur high overhead. Thus, static policies are considered as these policies provide a better performance than the fixed policies. Several static policies have been proposed in the literature. In a previously proposed adaptive static policy, the partition size is a function of the number of queued jobs. This policy, however, tends to underutilize the system resources. To improve the performance of this policy, we propose a new policy in which the partition size is a function of the total number of jobs in the system, as opposed to only the queued jobs. The results presented here demonstrate that the new policy performs substantially better than the original policy for the various workload and system parameters. Another major contribution is the evaluation of the performance sensitivity to job structure, variances in inter-arrival times and job service times, and network topology.